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The incidence of pancreatic cancer, the fourth leading cause of cancer death in United States, is increasing worldwide. Even though the cure rate has doubled in 40 years, it is abysmally poor at 6-7%. As surgical resection remains the only curative treatment and less than 20% of the newly diagnosed cancers are resectable, the major burden of disease management lies in early diagnosis, good prognostication, and proper neo-adjuvant and/or adjuvant therapy. With advancing technologies and their ease of availability, researchers have better tools to understand pancreatic cancer. In the post-genetic era, proteomic, phosphoproteomic, metabolomic, and more have brought us to a multi-omics era. These newer avenues bring promises of better screening modalities, less invasive diagnostics and monitoring, subtyping of pancreatic cancer, and fine tuning the treatment modalities not only to the right stage of tumor but also to the right tumor biology. As the multitudes of technologies are generating extensive amounts of incongruous data, they are giving clinicians a lot of non-actionable information. In this paper, we wish to encompass the newer technologies, sub-classifications, and future treatment modalities in personalized care of patients with pancreatic cancer.

Introduction: Many studies purport that obesity, and specifically visceral fat, impact survival after pancreaticoduodenectomy for pancreatic adenocarcinoma. However, these studies involve crude measures of obesity [eg, body mass index (BMI)] or visceral fat [eg, linear measurements on computed tomographic (CT) scans]. Some studies purport that weight loss and muscle wasting (ie, sarcopenia) presage poor survival in these patients. This study was undertaken to accurately measure and reexamine the impact of visceral fat, subcutaneous fat, and sarcopenia on pancreatic cancer. Materials and methods: CT scans of 100 patients undergoing pancreaticoduodenectomy for pancreatic adenocarcinoma were reviewed using specialized software to precisely determine the cross-sectional area (CSA) of subcutaneous fat, visceral fat, and psoas muscles at the level of L5 vertebra. In addition, linear measurements of subcutaneous fat and visceral fat were undertaken. Measures of cancer progression included tumor (T) status, nodal (N) status, American Joint Committee on Cancer stage, and overall survival after resection. Regression analysis was utilized, with and without standardization of all measurements to body size. Median data are presented. Results: The median patient age was 67 years, with a BMI of 24 kg/m 2 . Cancer stage was IIB for 60% of patients. BMI, CSA of visceral fat, CSA for subcutaneous fat, CSA for psoas muscles, and linear measurements of visceral and subcutaneous fat were not significantly related to any measures of cancer progression or survival. Standardization to body size did not demonstrate any relationships with cancer progression or survival. Conclusions: Precise and reproducible measures of visceral fat, subcutaneous fat, and muscle mass, even when standardized to body size, do not predict cancer progression or survival in patients undergoing pancreaticoduodenectomy for pancreatic adenocarcinoma. Pancreatic cancer biology and behavior is too complex to predict with a CT scanner. The main focus of pancreatic cancer research should continue to be at the molecular, genetic, and immunologic levels.

Since the Leapfrog Group established hospital volume criteria for pancreaticoduodenectomy (PD), the importance of surgeon volume versus hospital volume in obtaining superior outcomes has been debated. This study was undertaken to determine whether low-volume surgeons attain the same outcomes after PD as high-volume surgeons at high-volume hospitals. PDs undertaken from 2010 to 2012 were obtained from the Florida Agency for Health Care Administration. High-volume hospitals were identified. Surgeon volumes within were determined; postoperative length of stay (LOS), in-hospital mortality, discharge status, and hospital charges were examined relative to surgeon volume. Six high-volume hospitals were identified. Each hospital had at least one surgeon undertaking ≥ 12 PDs per year and at least one surgeon undertaking < 12 PDs per year. Within these six hospitals, there were 10 “high-volume” surgeons undertaking 714 PDs over the three-year period (average of 24 PDs per surgeon per year), and 33 “low-volume” surgeons undertaking 225 PDs over the three-year period (average of two PDs per surgeon per year). For all surgeons, the frequency with which surgeons undertook PD did not predict LOS, in-hospital mortality, discharge status, or hospital charges. At the six high-volume hospitals examined from 2010 to 2012, low-volume surgeons undertaking PD did not have different patient outcomes from their high-volume counterparts with respect to patient LOS, in-hospital mortality, patient discharge status, or hospital charges. Although the discussion of volume for complex operations has shifted toward surgeon volume, hospital volume must remain part of the discussion as there seems to be a hospital “field effect.”